Jean Honorio

Assistant Professor in the Computer Science Department at Purdue.
Lawson Building 2142-J, West Lafayette, IN 47907
e-mail: jhonorio at purdue.edu

Modern statistical problems are high dimensional (big data). My research in this area focus on developing computationally and statistically efficient algorithms, understanding their behavior using concepts such as convergence, sample complexity, and privacy, and designing new modeling paradigms such as models rooted in game theory. My theoretical and algorithmic work is directly motivated by, and contributes to, applications in political science (affiliation and influence), neuroscience (brain disorders such as addiction), and genetics (diseases such as cancer). [vita]

Prior to joining Purdue, I was a postdoctoral associate at MIT CSAIL, working with Tommi Jaakkola. My Erdös number is 3: Jean Honorio → Tommi Jaakkola → Noga Alon → Paul Erdös.

People (my students* and student coauthors)

Adarsh Barik, Kevin Bello*, Asish Ghoshal*, Yu-Jun Li*, Meimei Liu, Keehwan Park, Zhaosen Wang, Yixi Xu
Here is a note for prospective students that are considering working with me.

News

9/4/17. One paper accepted at NIPS.
8/17/17. NSF RI:Small grant awarded to do research on structured prediction.
6/29/17. Paper presentation at ISIT.
6/26/17. Coorganizing the 3rd Workshop on Algorithmic Game Theory and Data Science at ACM EC.

Teaching

CS 57800: Statistical Machine Learning: Fall 2017, also offered on Fall 2016
CS 59000-HLT: Hands-On Learning Theory: Fall 2017, also offered on Fall 2016 and Fall 2015
CS 69000-SML: Statistical Machine Learning II: Spring 2017
CS 52000: Computational Methods In Optimization: Spring 2016

Publications (by year / by topic)

1. Game Theory

Learning Sparse Potential Games in Polynomial Time and Sample Complexity. (Preprint)
Ghoshal A., Honorio J.
(Under submission, 2017.)

On the Sample Complexity of Learning Graphical Games.
Honorio J.
IEEE Allerton Conference on Communication, Control and Computing. Illinois, 2017.

Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions.
Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Florida, 2017.

From Behavior to Sparse Graphical Games: Efficient Recovery of Equilibria.
Ghoshal A., Honorio J.
IEEE Allerton Conference on Communication, Control and Computing. Illinois, 2016.

Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data.
Honorio J., Ortiz L.
Journal of Machine Learning Research, 16(Jun): pp. 1157-1210, 2015. [code]

2. Graphical Models

Learning Linear Structural Equation Models in Polynomial Time and Sample Complexity. (Preprint)
Ghoshal A., Honorio J.
(Under submission, 2017.)

Learning Bayes Networks Using Interventional Path Queries in Polynomial Time and Sample Complexity. (Preprint)
Bello K., Honorio J.
(Under submission, 2017.)

On the Statistical Efficiency of L1,p Multi-Task Learning of Gaussian Graphical Models. (Preprint)
Honorio J., Jaakkola T., Samaras D.
(Under submission, 2017.) [code]

Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity.
Ghoshal A., Honorio J.
Neural Information Processing Systems. California, 2017.

Information-Theoretic Limits of Bayesian Network Structure Learning.
Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Florida, 2017.

Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models.
Honorio J., Jaakkola T.
Uncertainty in Artificial Intelligence. Washington, 2013. [code]

Variable Selection for Gaussian Graphical Models.
Honorio J., Samaras D., Rish I., Cecchi G.
Artificial Intelligence and Statistics. Canary Islands/Spain, 2012. [code]

Lipschitz Parametrization of Probabilistic Graphical Models.
Honorio J.
Uncertainty in Artificial Intelligence. Barcelona/Spain, 2011.

Multi-Task Learning of Gaussian Graphical Models.
Honorio J., Samaras D.
International Conference on Machine Learning. Haifa/Israel, 2010. [code]

Sparse and Locally Constant Gaussian Graphical Models.
Honorio J., Ortiz L., Samaras D., Paragios N., Goldstein R.
Neural Information Processing Systems. Vancouver/Canada, 2009. [code]

3. Combinatorial and Continuous Optimization

Reconstructing a Bounded-Degree Directed Tree Using Path Queries. (Preprint)
Wang Z., Honorio J.
(Under submission, 2017.)

Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models.
Honorio J.
International Conference on Machine Learning. Edinburgh/Scotland, 2012. [code]

4. Other Machine Learning Problems

Compositional Nonparametric Prediction: Statistical Efficiency and Greedy Regression Algorithm. (Preprint)
Xu Y., Honorio J., Wang X.
(Under submission, 2017.)

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression. (Preprint)
Liu M., Honorio J., Cheng G.
(Under submission, 2017.)

Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity.
Barik A., Honorio J., Tawarmalani M.
IEEE International Symposium on Information Theory. Aachen/Germany, 2017.

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms.
Honorio J., Jaakkola T.
Uncertainty in Artificial Intelligence. New York, 2016.

Information-Theoretic Lower Bounds for Recovery of Diffusion Network Structures.
Park K., Honorio J.
IEEE International Symposium on Information Theory. Barcelona/Spain, 2016.

A Unified Framework for Consistency of Regularized Loss Minimizers.
Honorio J., Jaakkola T.
International Conference on Machine Learning. Beijing/China, 2014.

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees.
Honorio J., Jaakkola T.
Artificial Intelligence and Statistics. Reykjavik/Iceland, 2014.

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy.
Honorio J., Jaakkola T.
International Conference on Machine Learning. Atlanta, 2013.

5. Machine Learning for Neuroscience

Variable Selection in Gaussian Markov Random Fields.
Honorio J., Samaras D., Rish I., Cecchi G.
Invited book chapter in Log-Linear Models, Extensions and Applications.
Edited by Aravkin A., Deng L., Heigold G., Jebara T., Kanevski D., Wright S. (to be published on December, 2016.)

Predictive Sparse Modeling of fMRI Data for Improved Classification, Regression, and Visualization Using the k-Support Norm.
Belilovsky E., Gkirtzou K., Misyrlis M., Konova A., Honorio J., Alia-Klein N., Goldstein R., Samaras D., Blaschko M.
Computerized Medical Imaging and Graphics, 46(1): pp. 40-46, 2015.

Classification on Brain Functional Magnetic Resonance Imaging: Dimensionality, Sample Size, Subject Variability and Noise.
Honorio J.
Invited book chapter in Frontiers of Medical Imaging.
Edited by Chen C., World Scientific Publishing Company, 2014.

Improving Interpretability of Graphical Models in fMRI Analysis via Variable-Selection.
Honorio J., Samaras D., Rish I., Cecchi G.
Organization for Human Brain Mapping, Anual Meeting. Hamburg/Germany, 2014.

Predicting Cross-task Behavioral Variables from fMRI Data Using the k-Support Norm. (Best paper award)
Misyrlis M., Konova A., Blaschko M., Honorio J., Alia-Klein N., Goldstein R., Samaras D.
Medical Image Computing and Computer-Assisted Intervention. Workshop on Sparsity Techniques in Medical Imaging. Boston, 2014.

fMRI Analysis of Cocaine Addiction Using k-Support Sparsity.
Gkirtzou K., Honorio J., Samaras D., Goldstein R., Blaschko M.
IEEE International Symposium on Biomedical Imaging. California, 2013. [code]

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics.
Gkirtzou K., Honorio J., Samaras D., Goldstein R., Blaschko M.
Medical Image Computing and Computer-Assisted Intervention, Workshop on Machine Learning in Medical Imaging. Nagoya/Japan, 2013. [code]

Can a Single Brain Region Predict a Disorder?
Honorio J., Tomasi D., Goldstein R., Leung H.C., Samaras D.
IEEE Transactions on Medical Imaging, 31(11): pp. 2062-2072, 2012. [code]

Simple Fully Automated Group Classification on Brain fMRI.
Honorio J., Samaras D., Tomasi D., Goldstein R.
IEEE International Symposium on Biomedical Imaging. Rotterdam/The Netherlands, 2010. [code]

Learning Brain fMRI Structure Through Sparseness and Local Constancy.
Honorio J., Ortiz L., Samaras D., Goldstein R.
Neural Information Processing Systems, Workshop on Connectivity Inference in NeuroImaging. Vancouver/Canada, 2009.

A Functional Geometry of fMRI BOLD Signal Interactions.
Langs G., Samaras D., Paragios N., Honorio J., Golland P., Alia-Klein N., Tomasi D., Volkow N., Goldstein R.
Neural Information Processing Systems, Workshop on Connectivity Inference in NeuroImaging. Vancouver/Canada, 2009.

Task-Specific Functional Brain Geometry from Model Maps.
Langs G., Samaras D., Paragios N., Honorio J., Alia-Klein N., Tomasi D., Volkow N., Goldstein R.
Medical Image Computing and Computer-Assisted Intervention. New York, 2008.

6. Neuroscience

Methylphenidate Enhances Executive Function and Optimizes Prefrontal Function in Both Health and Cocaine Addiction.
Moeller S., Honorio J., Tomasi D., Parvaz M., Woicik P., Volkow N., Goldstein R.
Cerebral Cortex, 24(3): pp. 643-653, 2014.

Dopaminergic Involvement During Mental Fatigue in Health and Cocaine Addiction.
Moeller S., Tomasi D., Honorio J., Volkow N., Goldstein R.
Translational Psychiatry, 2: e176, 2012.

Enhanced Midbrain Response at 6-month Follow-up in Cocaine Addiction, Association with Reduced Drug-related Choice.
Moeller S., Tomasi D., Woicik P., Maloney T., Alia-Klein N., Honorio J., Telang F., Wang G., Wang R., Sinha R., Carise D., Astone-Twerell J., Bolger J., Volkow N., Goldstein R.
Addiction Biology, 17(6): pp. 1013-25, 2012.

Dopaminergic contribution to endogenous motivation during cognitive control breakdown.
Moeller S., Tomasi D., Honorio J., Volkow N., Goldstein R.
Society for Neuroscience. Washington DC, 2011.

Disrupted Functional Connectivity with Dopaminergic Midbrain in Cocaine Abusers.
Tomasi D., Volkow N., Wang R., Honorio J., Maloney T., Alia-Klein N., Woicik P., Telang F., Goldstein R.
Public Library of Science, PLoS ONE, 5(5): e10815, 2010.

Oral Methylphenidate Normalizes Cingulate Activity in Cocaine Addiction During a Salient Cognitive Task.
Goldstein R., Woicik P., Maloney T., Tomasi D., Alia-Klein N., Shan J., Honorio J., Samaras D., Ruiliang W., Telang F., Wang G., Volkow N.
Proceedings of the National Academy of Sciences, 107(38): pp. 16667-72, 2010.

Dopaminergic Response to Drug Words in Cocaine Addiction.
Goldstein R., Tomasi D., Alia-Klein N., Honorio J., Maloney T., Woicik P., Wang R., Telang F., Volkow N.
Journal of Neuroscience, 29(18): pp. 6001-6, 2009.

Anterior Cingulate Cortex Hypoactivations to an Emotionally Salient Task in Cocaine Addiction.
Goldstein R., Alia-Klein N., Tomasi D., Honorio J., Maloney T., Woicik P., Wang R., Telang F., Volkow N.
Proceedings of the National Academy of Sciences, 106(23): pp. 9453-8, 2009.

7. Other Applied Areas

Integration of PCA with a Novel Machine Learning Method for Reparameterization and Assisted History Matching Geologically Complex Reservoirs.
Honorio J., Chen C., Gao G., Du K., Jaakkola T.
Society of Petroleum Engineers: 91th Annual Technical Conference and Exhibition. Houston, 2015.

Integration of Principal Component Analysis and Streamline Information for the History Matching of Channelized Reservoirs.
Chen C., Gao G., Honorio J., Gelderblom P., Jimenez E., Jaakkola T.
Society of Petroleum Engineers: 90th Annual Technical Conference and Exhibition. Amsterdam/The Netherlands, 2014.

Two-person Interaction Detection Using Body-Pose Features and Multiple Instance Learning.
Yun K., Honorio J., Chattopadhyay D., Berg T., Samaras D.
IEEE Computer Vision and Pattern Recognition, Workshop on Human Activity Understanding from 3D Data. Rhode Island, 2012. [data]

Digital Analysis and Visualization of Swimming Motion.
Kirmizibayrak C., Honorio J., Jiang X., Mark R., Hahn J.
The International Journal of Virtual Reality, 10(3): pp. 9-16, 2011.

Digital Analysis and Visualization of Swimming Motion.
Kirmizibayrak C., Honorio J., Jiang X., Mark R., Hahn J.
Conference on Computer Animation and Social Agents, Simulation of Sports Motion Workshop. Chengdu/China, 2011.